Workload: Our workload is generated based on Facebook trace
[1]}, collected from a 3000-machine, 150-rack MapReduce
cluster at Facebook. This trajectory is widely used in simulation
[2]}, [3]}, [4]}, which contains 526
coflows that are scaled down to a 150-port fabric with exact inter-arrival
times. We divide 526 coflows into training set, validation set, and
test set at an approximate ratio of 8:1:1. In each set, randomly select
several coflows, and construct the job DAG by randomly generating
an adjacency matrix. For each coflow, the Facebook trace contains
sender machines, receiver machines, and transmitting bytes at the
receiver level, not the flow level. Thus we partition the bytes in
each receiver to each sender pseudo-uniformly to generate flows. In
addition, the trace only contains the information of each coflow,
not the job DAG. Therefore, to evaluate our algorithms in the scenarios
of dependencies, we randomly select coflows to form the job DAGs so
that each job contains \(n\)  coflows in expectation and n-1 dependencies
are generated randomly. We randomly selected \(P\)  machines from the
trace as servers. The arrival time of each job obeys a Poisson distribution
\(P(\lambda )\) ; the weights of the jobs follow a uniform distribution.
